Bootstrapping At Scale

One famous Silicon Valley axiom is that nothing is a better teacher than being cash strapped. And many venture-backed firms who have closed huge rounds have gone on to make massively costly mistakes. That is why we promote strategies that achieve scale and triple digit annual growth with self-funding programs that require very little investment capital.

Sales & Marketing Should Power Themselves

Several of our startup and growth-stage firms come to us after their home-grown sales and marketing strategies have not driven projects returns. Often they plowed large budgets into activities that were for “long-term” goals or with long payback cycles. Simply put, don’t do these. Make it clear that sales and marketing are there to feed the organization, not the other way around.

Don’t Spend Money if The Results Cannot Be Proven

A professor once told me that “strategic” is a management uses when they want to do something that they know will lose money. Internet marketing and direct selling have the advantage of offering lots of data-driven activities. Insist on understanding the marketing pipeline and what the metrics are so you can trace every dollar from initial lead generation activity to receipt of bookings.

Short ROIs are the only ROIs

Digital marketing spend should be able to result in profitable dollars in within 90 days or don’t do those activities. Prioritize scalable, low-cost selling over high cost selling at all turns. Also prioritize scalable solutions wherever possible. Don’t do expensive, outside enterprise selling until you have concluded there really is no other way to grow. one of our clients achieved their first ever profits by cutting outside selling investments and focusing on new digital marketing and inside selling tactics that drove higher volumes of easier to collect subscription sales. They built all this while becoming profitable with relatively little up front incremental investment.

Stay Home First

Human nature means doing what you like and what has succeeded for you in the past. This often leads leaders to emphasize higher cost, slower sales models than they need to. If you can make money by direct, automated selling through digital marketing then optimize around that and scale as large as you can before looking at other selling models. Look next to inside sales models before looking to outside models. Outside selling is a critical practice for many firms but is typically the slowest to grow with the longest sales cycles and the hardest to scale so do not assume it is your only option.

Beware Outsourcing

There is a time and place where outsourcing can help with scale but never when you are early stage or unprofitable. You must be able to generate leads and close them yourself before you can outsource that to another company. Similarly, indirect selling such as though partners or channels has its place but should be a last resort. Nobody will ever care about your sales growth as much as you do.

Slice data, then slice again

Everyone wants to be data driven, but doing it successfully means slicing data and looking at cohorts while eschewing averages. Averages hide treasure. Encourage large amounts of short, inexpensive tests. It could be that one lead traffic source with one landing page with one call to action with one inside sales strategy and script is massively more profitable than the others. It is not true that you can treat marketing and sales as distinct activities. Leads generated from different lead sources rarely will respond the same to your sales strategy. Assume they will not. Even small changes in copy or call to action can convert different leads or set different expectations that cascade to very different sales experiences. Drill into that and plow your money into that cohort to scale it. This has allowed us to make many, many clients massively profitable on strategies they thought were disproven.

In The Next Decade, AI Bias Will Do As Much Damage As Data Breaches

Data breaches are a terrible risk to enterprises. We run off data and a breach mean a failure in the commitment to safeguard data. It leads to hug costs to an enterprise; civil and sometimes criminal liability can follow. Allowing bias to creep into AI algorithms can have a similar destructive impact on the firm and, as I wrote this week on CIO.com, I believe that the next 10 years will prove that out.

Reliable Data

An AI system is only as good as the data we provide it. If we’re providing it with biased data sets, it’s going to produce biased outcomes. Software engineers and project managers have to be conscious of this possibility and they have to work to prevent it. If the system starts with a clean data set, we can be confident that it will provide valuable assistance to a business organization.

Introducing Systemic Diversity

It’s probably not enough just to avoid biased data sets though. AI is a technology that’s designed to grow and evolve – we must work hard to ensure it can recognize the diversity of our society and organizations. Recently there have been stories in the news of facial recognition systems that have trouble with anyone that isn’t white and male. If we’re going to avoid these issues, AI systems must be introduced to as much diversity as possible.

Training and Testing

As humans, we aren’t born with personal biases and prejudice. These are things we develop over time as we’re influenced by our family, friends, and society. The same thing can happen in our AI systems if we’re not careful, no matter how much care we take to avoid it in development.

If we’re going to make sure that AI remains free from bias in our business systems, we have to be constantly re-evaluating and upgrading. Programmers must be trained to spot it and the average end user must have the tools to help correct it as well.

Automation Will Destroy Jobs – How Do You Get Organizational Buy In For That?

In his latest installment on his CIO.com column, Cloud Commerce Consulting CEO, Michael Zammuto talks openly about automation. Enterprise IT initiatives need business partners to succeed. But AI focuses on automation and that means job destruction along with productivity gains.

Many AI initiatives offer a new challenge. We have the ability to automate not just manufacturing, field, service and support jobs but, increasingly white collar, leadership and technology roles. He argues that this means AI will challenge the empires and possibly the careers of the very people you need to drive the initiative. In his article, he talks about innovation, buy-in and the reality of white collar automation.

AI is Different Because Scales With Data

Cloud Commerce & AI Can Help Make Projects Scale

Cloud Commerce Consulting CEO, Michael Zammuto wrote recently on his CIO.com column about the impact of data challenges on IT initiatives. Mike argues that many promising IT initiatives stall because they depend upon data being cleaned, enriched or combined as a precursor to success. BI and analytics projects are particularly dependent upon good data. Even with the best traditional tools and techniques, the process of preparing data for these projects does not scale well, often negatively.

This means trouble for IT chiefs and business sponsors alike. Mike argues that because machine learning algorithms can learn to categorize and clean data better as you as it processes more data, this means that AI is the only approach that scales data projects well.

Exec Summary and Reference Sheet on AI

Cloud Commerce CEO Michael Zammuto published a new article on CIO.com entitled 8 artificial intelligence technologies your enterprise needs today. The article is an executive summary and quick reference for CIOs and other functional, technology and business executives who are interested in understanding the most critical aspects of this crucial and transformative technology.

“AI” Is An Overloaded Term

Zammuto argues that the term “Artificial Intelligence” is an overloaded term. To insiders, academics and researchers this term is shorthand for general artificial intelligence which is the common view of AI as a system with intelligence that typically is modeled on biological cognitive systems. General AI is not a commercial product yet and is confined to research projects, predominantly.

Understanding The “AI Landscape”

Zammuto argues that, for enterprises, the term “AI” is more reflective of an “AI Landscape” that includes many interconnected technologies, available from a broad range of sources and vendors. Of these are ones that get a lot of media attention, including machine learning and automated assistants and chat bots, as well as less broadly appreciated technologies with massive potential including natural language generation and decision management.

Michael Zammuto recently published an article titled Three Entrepreneurial Skills I Mastered and The One I Never Will. The article touches on critical, behavioral skills that are critical for entrepreneurs to be successful. The article also introduces the concept of lifelong learning. This discussion illustrates the Cloud Commerce philosophy that some skills can be mastered while others represent an opportunity for long term development, even in very senior, experienced and skilled leaders.

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Global Lean Ecommerce

The internet startup community was revolutionized by the lean startup movement. Combined with Agile software principles and cloud based infrastructure and services it became faster and easier than ever before to launch your startup.
Ecommerce companies now have the same thing. But successful operations means that you need to build an ecosystem around these disconnected cloud based commerce solution. Cloud Commerce does that for you.

Technology Transformation Strategies

Review your industry landscape and look for how your company can be disruptive not disrupted. One of history’s great technology revolutions is underway. Machine learning, general artificial intelligence, virtual agents, speech recognition, natural language generation, decision management, big data, deep learning, and predictive and descriptive analytics are going to rewrite the way the winners in every field innovate.